{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fall / Non Fall Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Rows, Columns) in training dataset (480, 136)\n",
      "Number of Features in training dataset:- 136\n",
      "Number of Features in testing dataset:- 136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn import ensemble\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_selection import VarianceThreshold, RFE\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Random values generated should be same everytime the program is run\n",
    "np.random.seed(7)\n",
    "\n",
    "#Reading of Training and Testing Data from CSV File\n",
    "data=pd.read_csv(\"Fall10.csv\")\n",
    "train=data.iloc[0:480,:]\n",
    "test=data.iloc[480:,:]\n",
    "print(\"(Rows, Columns) in training dataset\",train.shape)\n",
    "print(\"Number of Features in training dataset:-\",train.shape[1])\n",
    "print(\"Number of Features in testing dataset:-\",test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "Replace NaN values with minimun of that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#Transforming Columns containg \"NaN\" Values\n",
    "#in both training and testing dataset\n",
    "for col in train.columns:\n",
    "    val = train[col].min()\n",
    "    train[col]=train[col].fillna(value=val,axis=0)\n",
    "\n",
    "for col in test.columns:\n",
    "    val = test[col].min()\n",
    "    test[col]=test[col].fillna(value=val,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Train and Test Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Separating training data Features and Predictions into different dataframes\n",
    "X_train=train.drop('class_label',axis=1)\n",
    "Y_train=train['class_label']\n",
    "\n",
    "#Separating testing data Features and Predictions into different dataframes\n",
    "X_test=test.drop('class_label',axis=1)\n",
    "Y_test=test['class_label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7853e322b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sQZ9kDfB3wKXAucBVSc4dx3tJkhY2riP684Dpqnq8qv4XuA3YMqb3kiQtYMk3Bx9iHbBv\n3voM8K75A5JsA7a11eeSPDqmXk5EZwI/X+0mhstqN6Bj7/j42cxx87P5u6MMGlfQD/qvVL+xUrUd\n2D6m9z+hJZmqqsnV7kM6kj+bq2NcUzczwIZ56+uB/WN6L0nSAsYV9N8FNiU5J8kpwJXAzjG9lyRp\nAWOZuqmqF5JcB/wrsAa4tar2jOO9NJBTYnql8mdzFaSqho+SJB23vDJWkjpn0EtS5wx6SercuM6j\n1zGU5C3MXXm8jrnrFfYDO6tq76o2JukVwSP641ySjzH3FRMBHmLu1NYAX/XL5CSBZ90c95L8J/C2\nqvq/I+qnAHuqatPqdCYdXZKrq+qLq93HicIj+uPfi8AbB9TXtm3SK9GnVruBE4lz9Me/DwP3JHmM\nl75I7neANwPXrVpXOuEl2X20TcDZx7KXE51TNx1I8irmvhp6HXP/E80A362qQ6vamE5oSZ4E3gM8\nfeQm4D+qatC/RDUGHtF3oKpeBB5Y7T6kI9wJvLaqdh25Icn9x76dE5dH9JLUOf8YK0mdM+glqXMG\nvSR1zqCXpM79P/hY7DvHtOZyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7853e6f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.class_label.value_counts().plot(kind='bar',colors='YR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "### Remove Low Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features in training dataset(before feature selection by Variance Threshold):- 135\n",
      "Number of Features in training dataset(after feature selection by Variance Threshold):- 132\n"
     ]
    }
   ],
   "source": [
    "#Performing Feature Selection on Training Dataset\n",
    "print(\"Number of Features in training dataset(before feature selection by Variance Threshold):-\",X_train.shape[1])\n",
    "X_train_temp = X_train.copy(deep=True)  # Make a deep copy of the Training Data dataframe\n",
    "selector = VarianceThreshold(0.12)\n",
    "selector.fit(X_train_temp)\n",
    "X_res = X_train_temp.loc[:, selector.get_support(indices=False)]\n",
    "X_train=X_res\n",
    "print(\"Number of Features in training dataset(after feature selection by Variance Threshold):-\",X_train.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between each feature and class variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find most important features relative to target through correlation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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dxaxX4VEHfSmpXzN7v7zRwHb4+znJMhfYEv+FG7KVk5y/4rPWXD6FNxK5OMl5\nIXk1vjs0vgmb2U3ATZJON7OcGekoJO1nZt8B1u/lXqnrUkmyNsGfPFY3s+ekSKndzewTmWM6Ei/L\n/Az8Glwadxv1DaksqJcXmdlFwO/VI3PW6odI/4+ZvQv4sqQxLifLj1ApMakoNVkaxcD43CVdbmbb\n9/DzLc6yAc/D/cGjjE+6YHNlLYN/OYTXK8lKu67I2QKP6RUetXBDzfMGTr8LB5A6Z1lGdmAPGWvg\nj8jCk6P+2EDGzrjxGnUTNrNeUVPd555tZvtonOxQq58V+nYzOzEZ1DHkzMolXYpPLE7srKmoK2a9\nppwb8Zrw11fk3FznPRXUy1FmdqRahjhL2tLMrisRoZJcncfi6wALJxVmdlZdGRVZ1clSdjRST5mD\nYtxLIekDZnacpC/R+2LKTultanw6s41eM40kr+5sYyUze1DSU8aR07qWSc1xFLlBdGan4y38ZSz4\nPdPM7kg3vF5ymoT8NboJS1rDzP6gAuGqpZB0rZk9v7pgrgaJN5J+aWZbdRZAJa2A50TUMe4DpxeA\n9B4eqbhUpuBFB8eEWPaR03pSMVkMjFumg7wv5q1m9lDaXhF4tpldU1NEp5bM7BZj6Gl8JHdvZzwa\n74AnW7yyx9+MkaiXfpyBh0hdRw+jCuTEYTfWr5ltn362XWhbIf3sJSdntvEevHRwr9o6htfA78sE\nN+GNJNW6CdtIktESwB/M7NEkezk8zC2L5L89zFLtH0mrAJ+rO0NN/FnSRiSdSnoNvu6Sy9mSTgSe\nLOltuHvz63VOnAS9fBI4rksv7zWzj2aKuhBvuff3tL0ccAEeSdNvDN2Tik7c/pqS1qw7qZjsp+mB\nm7lLugFvtda5IJcAZlvNkKmKnL3N7Jx++8Y5d6JHY7OMKoxJ3gbdi0+99i0KSuhX0mlm9oZ++2rI\nGZM00mtfDTnLdozGRPsmOL/II3+SNRt4gZk9lraXxuscPX/iM8fIGROe2mtfHxkbAifhBut+PB9h\nPzP7bc5YkqxdgJfghud8M/tZ5vmTqZfaIZWVc8Y8wdR9qpF0kpkdqAFLchyDLcbEhl4vehSBIqOV\nXOWc1kkKwHZ19jUcS3YxMQokcJTQb/f7wZ8Ab1scn1FhORvU2ddAvzc1GMtNwCqV7acAt+TKSeeu\nQI+a6ovyVVAvN1Pp2YDPuGt1UOqScwWVfr14kbirMmWMaVzTa18NOafV2Zf7Gji3DDBP0qH4Kj/A\nO/AFrlqoXFwtePx494yg177xxvJMvO3fyl2P/Cvh3V9qoZEEjtXSY2g1gWPNunISjfUr6UN4rPVy\nkjrrD8LTr0+qOwCNJJJM7XIMlOzBAAAgAElEQVR9rYTX8qkrp5Pws5ykzRmtl+XryqlwHmM/23Px\nL35dFkja3cxmpjHuAfy5wVg+B1wpqRPrvDfeCasvpdyKhd0GpfTyHeDC9JRluIvo1IlP6cm7gHMk\n3Zu218CjVHK4krHXS699/Xh2dUPSkuRdcz0ZRON+EF6psOND+znuV61L67jaUsYHX5h7BR4uVfW7\nP4RnmtalmsBxHSNGLDeBA1ro18w+BXxK0qfMrEmoYYdSiSQlEn6K3YQTBwGnS+p8LvfgyVBZmNm3\nkyuj84i/p9VvKjHRmkbOGEqtsUA5vRwn6WbcXw5wjJmd30DOtelzry6e18pELjWpKDVZGld+egQY\nOiQtVffD6nHuDniK80F4k40ODwE/MrNfZ8rb1jJKmk4gp1UCR0nSE8TGVIyfmf0iU8Z6ViBaQi0S\nftL5e+A5A7vjlQo7PIQXqbqy54kTy1wR/3491GJcm+HhdYa3QmyS1dwYSXtaWkyWtIqZ3V9AZgm9\nrI7X3DcaFq1Ti1ITkvbHJxXTGR248RBewbN2WfEkr+1kqbfcQTPuktbGXR/b4R/e5XjUwPwJTxwr\np9OQYhqjDVBOZEkp47MsXjrg2V1jyW4DJ+k5jH1PtdtxldCvvBvOYfiM+Ua8W9BVll+qdyrwAcbq\nJXtBStLLe8jJXfhufRNWuXpEnTpC5+Gzudp1hCRdYGYvSb9/KD1xZVNdqGyyaNklq5ReitRhUYF6\nT20nFV2yWk+WxtDWaV/6hdcY77TRWhK/Q/6sgZzLgZ3xBZj18HZ5R2XKmIpfSLPwkMaLgIsajOUc\n4Bi8A83+eMjV8Q3kHIknTPwJT7b5I166d5HqF7glXYQ3pu1nAt9t8H4uwG96t+Nho98EPt1Aztfw\n6pT3JB3dgtfpzpWzLPBOvLbMNzuvTBnnAUfh4akbpvFkdS1Kcm4GVqhsr0DNhW/ghsrvjSsddsm5\noamcwnq5iUrVxPQdbbRgXWdfDTkvxycoR3ReDWS8NV2z96fv9yNN7MwYuW0FlH7Re1U9u40aKRqF\nSoQB/mibI6OU8bkh/bw5/Vyq4U3iFjxe+Ka0vTruJlqk+gWu7ZxHilxo+RndXNl3aQM5N3f9XBG4\noIGc1jfhgtfvLVQiL9KNp1a0TNWgtzTud+CZqVum78Dm+GLhFlQiTRa1Xrq2l6irl24d4bWjOtsb\n5uqKcpOKIpOl7tcgLqiWKPgF8GiK4f61pIPxHolPzZSxqpmdLOkwGyk41KSAfseP97fkVvkj3tor\nl0fM7N+SHpdnzd5HRgJTooR+50t6Mt7C8GeS7scXsnPp6OUPya1yL+7qyaUTz/4PeUepvwC1Ovt0\n8XQz21vSHmZ2qqQzgNzFulL1iE6hYR0hYEN55U9Vfl+I1a+f8gdGFqr/yOhFa6NmkliilF5K1WEp\nUe/pBWa2qbwUw1GSPkf9xMQqj5rZo5KQN2S5Q1KtjlITMYjGvUTBL/DokuXxJhnH4B3b98+UUcr4\nnJR8ah/FF+xWBD7WQM7sZFS/jkfN/B1vzZVDa/2a2avTrx9PiRwrAz/NHAfAJ5Iv9r34OsBKZFQK\nrPCjpJfP4DMyo2YGZRclbsJFiqGZ2eclXcJIHaE3W806QsAeld8/m/u/K2PYqem5PSillyJF68zs\nwrQu16beU6lJRanJ0igGbkG1BPI6Ecea2ftbynkFcBnehLdjfI6yFKtbU8YSeJeis1uORXgnqXvS\n9vrASmbWpBxym3G0ao5ckTMFbwr8hb4H9x/PNpYiWuS1YZa1zIW6dO5bcd/wc4FvkW7CZnZiA1lN\n6xH1rB/UwfL6uZaqn/JO4HQbnfK/r5l9deIze8pqXSSuDRqnzlMHy4h0kfQx3C7sjIckG/B1Mzui\nxfh2IE2WLGXzNpY1KMZd4xT66mCZBb8kXQTsbA3fYCnjk2T9wsxe2P/IvnKuM7NGyQ0l9SvpdLz6\n3e+ajKUi5+ISs0NJV5lZq1LGbW/CKtf96C78c6rW6e9sm+VFe10NvNjM/p62O2sRfeundMnplapf\nqxRCQb0U6Qql3iUmKsOpXV2yyKSi1GSpF4Pklmlc6GscbgB+KOkc4OHOzrp3ZjN7Qt5Cq7Vxxx+1\n3gd8t2ssudUcr5b0fDO7tsEYSup3DeBWSb9k9PvJrYV9paQvM1YvudUcL0iP6t9rejNPaxkHA02f\nsEp1P2ryWD8ey3YMe5L9d0lNMneXkKSObtPEZ+ma5w5MV6gkp0kfhV5y/p187Num7X8C2WW8k5yb\nJK3bdrLUzcDM3LuRtIKldnINz+91h659Z04y/ht/RGplfNJsrNdYshZDJd2G+wh/m8bTmbXUqond\nJauxflWoW7sKFV7SSBf6J/BFukZV9dJj9iO0vwm3JrnhXo/XtjlG0rrA08ys9hqLpCuAQzrXq6Qt\ngS/nPuVI+gy+9vA1fPZ8EHCPmb03R04p1LwrVFXG6sAngTXNbDdJ04BtzazuojWSjsJDVhtPKpKc\ni/CywW0nS6PlDppxl6f+nwysaGbryrP03m5m71gMYylifEqhAjWxS+k3jWVjM/t5mg1OsRZZh4NA\niZuwynU/OgH4N/AiM3tW8nNfYBlVFCU9HziLkcW5NYDXmtl145/VU84SeAmMnfEb5wXAN8ysdqvI\ngno5ktQVysw2SQuZ55hZ365QXXJ+gkckfcTMNpPXc7nBzJ6bIaPUpKLIZKmXgIF6AdfgC5jVBIpf\nNZCzCV6z+Vdpe1Pgo4vpPS2PR8qclLY3Bl7RUNb2eOQEeAJHbtXC1vrFMyevBX5TeT9Z1SnTeavj\nN5qfpO1pwAEN5AjYD1/8JL2/rRbTZ30pnhrf9vq9Pv2symmSZLMU8Bx8kXipxaGTwnq5MX3eVTlN\nqsZ2cjWqcrLj7gvqZz18faRjL1pX8WzdhHUysBQRUqFJM+mv4300/5Vk3gzMyBEgaXVJJ6e7PJKm\nSTqgwVhOwYsBdRay5gNZM5b0/48EPshIf9Cl8Cp5WRTQ7zvxULQHk7xfk59DAB6Rcj4jlS3vxENY\nc/kq7vt8Xdr+O/kF1ZC0vKSPSjopbW+cIqZyWN7Guk6a9A79V/Jtd/zcU/GZfF8kvSj93BMvWLcJ\nfgN+Zb9okS45Z6eft0i6ufuV93aK6eUxcwvY0csKfY4fj4clrVqRsw2QuxgqSfsldx6S1pG0Ve5A\n5A1QzgU6UVlr4WGRrRikBdUO90h6AWDygv6HMtJdKYflzeyXUjXoIPti+hbp0S1t34n7Y2v75RIb\nmdlr5T0XMbNH1DWwmrya1MsyyblXUu5CUwn9/tPMHuu8hfRI28S/t5qZnS2vjoeZPS6pyY18a/P2\nbzckOfen95bLKXj+QPUmfA7w4wwZpboffRH4PvDUtPbzGkYqefZjB8p0ADss/cy9wfVisXeF6uI9\neM7JRmltYip5FUnBJxX/xpO5jmFkUpHVgASfLG2FP1VjZr+W1GSyNIpBNO4HAcfjd6/5uH/vnQ3k\nlLiYShmfx+RtxTpj2YgGK+ukWYtS1/aGs5YS+r1UUqdU6S54TfgfNRhL69lTovEst4sSN+F34uVa\nnynp93j3o9fnDsTMTpd0HSN+7leZWa2bsJkdmX492np0AMsYQ+f78g4z+2CXnE/jT5F1KaWXz6Zr\n7kE8uOAIy+gKpVQt1syuT77uThLTHMuvIltqUlFqsjSaxeVj6uFzml5Y3oZ4rfJ/4KUHLgfWz5Rx\nCbAqI/7PbWhW+2QX3Oe4ADgdj3bZsYGc9+GPbvNwv/dVeDTEItUvXs/jbfis9tz0uxrI2QLviPNA\n+nknsGkDOa/HZ2Hz8YYWc4C9G8i5Eu/s0/m8N8JLytY5d/Wu7Ubdj4D/Te9nhdxze8gq1QGsl5y6\nRcxK6eXLeLp/W53ch8/0X9Tkmu2SdQ3e36FzvUylQYE14Di8rvsdyVZ8H/jvtu91YKJl0t1vRbxm\nxJlWc5ZSQ+4KwBLWIJJD3gD3S/iC1K9Ij27WICs0zVC3wWcJV5tZky40qGEvy8nSb1vSLKXN7Kkj\n55mMzHIvbPL+km4/ii/sXoCvK7zJzC6pce4f8QJQZ+KVOhtlYMpry8/Am1FclOTNsoxsRY00HzkO\nr6HSYSW8PO6ze544Vs5/4U9lG+LF1Do8CbjSzPrOvAvq5TBcL2vgrtEzzezGBnJWxd0vM/B1iHOT\nrL4N4nvIej1e22YLvHTwa/Cgjb59mrvkLIEXKFz4vcajkVoZ54Ex7gDyYjkzcIU9hl8QZ1leqF+R\njLiKvMbGRyPd0ccbS26yTiva6lfSLUyc5Vor3r7fop7VTDRTwVT9isxGN+HkFnoxrt+X4U9VZwIz\nzSy7QFZy4+2e5G2LF8c6s87NXIWaj8jrwKyC90U4vCqnrm4nQS/rJVkz8EqKnWv4zgay1sTbF87A\nAwLOMrOPTHzWGBmtJxWTxUAZ9yry+OsZwD7AH61mHGuKKBkXMzuqhoxSxqdXnHxFTL14eRVKve6S\nma1fjRNn3yHjJlEqBbxIqn7pm3Dyu+6G63cn/Euf7V+uyNsUnxluamY5PWaLdABLskokDpXWy+Z4\nGe4svXTJWBHYE19gXcPMVq9xTpFJRanJ0ngM4oJq5zHlqXgc9Aq4r7oWdYx3DXpFGSz8F9SMNrBC\nVfWsUOp1h6b6rRpvjU5iWo6Ma8nKpYCXStX/3ET/hrzStpgvjt2GRyFtibt5spBnUO7DiCviHPJL\n0v5F0oUUTBzCI4qWxkNwsxKHCullKWBXXC8742tZWd95eWe0V+LlrrfDK5p+CHfF1eE6JphUUL8M\nd4kopPFp67Qv+cJbZn0Vz6i7AA9zWrmhrP8LSUyrkZHEVEq/DH4S07oshiSm9H8/gIeqzsGNzrMa\n6PYiPAjgS8B2LcYzEIlDhfSyCz5Lvw+PzGq06AyckWSci/vIl82VMUnXTjWJaTkKJDEt9jdVeXP3\n4BETB9O1wt5QXusLu6Dx+W66uDs3muVo1oXmyHRh35m21wSuWNT6TV/2pbt026Qbzk/w2Wmns9SS\nDeWcgMcX3562VyFlIGbKaXwTxiNt7sbrpzeOTMJnxi8B1m3zGSVZRbIwSRFDjESF5LT8K6WXi/Eb\n31Na6mR/fGF5nwL6LTKpoNBkqfs1SBmq2+PNc9czsz8VkFciI+5blMmg3MjMjmMkW7ZThyKXV+OL\nZA8nOfdSv+peSf3+0yrRG22TmEgx6Wb2OM2ykbc2s3eSmieY2f3Ur1pY5RSaZxJ/CHg68Hsza1yB\n08zebGYX4OFwbZmsxKGfUz9xqJRedsInWq2aRpvZqeYROwe3kZPozox+iAaZ0ZTL+B7FwBh3M7vb\nvBDRlg2zN7splsREe+NTNImpIqd2ElNh/XYnMZ3D8CQxNboJm9ml5pFUrSr5VbhaXvirDe/E8yI6\niUPvwjsiZWFmn8XdGOcxkjj0pZrnFtOLedORm+QVMtvyM0nvk5cMeErnlSmj1KSi1GRpFIO4oNqq\nDnuFXhlx+2XKKGV8jsQXbdaRN7rYjgYtxiiTel1Cv4fjcbm34NUCZwHfyBwHlEkBh3ap+lVK3IRL\n1ajfCXi7pLtpWN7ZzOYBL26T61GR9TOgdiZoD0rppVQvgU5EVjU7O2cxFMpNKronS00zvkcxcKGQ\n44TJmWXUYe+SF0lMo89vrd+k00fTk0Anljm7fVs6dyiSmCoyeoW/muXXqO8Zdmp5OR/LAHvhtdgX\nTuTM7OjMsewJfBp3FYgGIbgF9bJDr/3WtjxuAyKJaTEh6ZPAcTa67+N7zSxrRlfC+Eh6NXCRpRZc\n8ma4O5pZVuU3eV2QP5jZo2l7OXxx9Le5Y2qDyrVvK9KbMz1R3dq5gcuLqU2zZlmHRW7CpZAXkFq2\ns20Z3Xok/RR/0ryOijvRzCYK/ewlZy7wyiY3zEFG3gR9GqP1++1MGSUmFcUmS6PkDppxTzGoB+Dp\n01WlZ83c1aPHo6TrzWzChJWu40sZn8Y9KLvOmY3X13gsbS+NR8vkNHBord9x3s+YfQ3lNNHLDcAW\nnZlOmgnNzvms03mlbsIvZ6x+c2fLu+Px92vioXvr4dFAtUoHJBm/sgK9OSVdYZnNMMaRU0Iv2+BP\n08/C/dtTgIdzniKSnCOBHXHjPgtPrrrczGq7BUtNKkpNlroZmAXVCqcBTwNeioczro2vQucyJT2W\nAgtnuctMcHwv3tYx7LBwweRtDcbSS89N1juWrC68pN9zF3BK6PdhVbI65e3bslPJSb05K3JyenNW\nWdjfExYuvDXR75FWaXCcPvsJM57HDET6Gv6ofgg+m9sbN8y5HIM/Qdxpnqy1Mx7KmsOVkmp3FpqA\n2ZK+K2lfSXt2XjkCCurly3jy0a/xkOK3pn25vAbX6R/Nk+o2I98+nICX+e3wcNqXy5het3hYbisG\n0bg/3cw+ht+NTwVejneRyeU7wIWSDpD0Fnwx6NRMGaWMz2xJn5e0kaQNJX0Bf1TOZUGa0XXGsweQ\n6zYood93AedIukzSZfgiWZPQsvPxReKd5Q0mzsQXnnOZJ+lQSUul12F45cxcStyEX2BmbwTuN8+W\n3hbvDJXLv8zsL/g1uISZXQxkPRnh4a/XSZojb7Bxi/KbbIDHhf8D9wm/Mr1ysytL6QUzm4u3dXzC\nzE7BZ+C5PJImAY9LWgl/OsrqaUy5SUWpydIoBjFapuPT/lvyif0RXxDKwsyOSxfyi/GZwjFmdn6m\nmI7xqTYGbmJ8DgE+hhtB8MW6JtEcBwGnp6gD8DjsN2bKaK1fM7s2+Ro7axF3NFwI/SBwIB6et7A3\nZwM5B+ERMx/FP6cLk9xcZkv6PB6rbPjnlnsT7nwp/yEvTPUXoEmZhL+lx/Nf4J/5feTnaezW4P+O\nwcqUiyill38kd+SNko7Dw5ub9DWYndxuX8c/47/jDapzmCfpUEZm6++g2aSiM1ka1eu2gZzRWMss\nqNIv/DFrFbybzDz8jnpQAzkbUEktxh/h1s+UsQRuODoxvm/HZwyLW0cr0jA9uYR+8fCxJ1e2V8Eb\nOuSOZYWqPnH/6fKLUa8rAMcCs9Prk2SmuOM38SfjUSp/xI3PMU11g0/A9sc7Zq2aKeMpPV7ZfVTx\nG2f36xhgj8Wgl/XSd3kl3GX2efxptM3nvj7N+gg8FW9Afh/wJ7y0wVMbjqF4r9uBW1AtRaHFxyKr\n2JJ+hjePqC7MnmVmL82UUyQCqC0FF0JLRd2cChzWpZfPWcPw2VKkNZ9lreLHX8T//7e46+N+/Mno\nybhRvQ9fT6r1VCLvKftMPFkN3EDfmmTPM7OsrO3FqRcNWBluKBe40c3AuGVUuA47PRYfld8C60Lc\nrdNZ7FgOdx3krmKvZl0Ls2rWI3E3M/twl5yXUcPFU1i/S0ha6G9ssRYxZiFJUpOFpE176DfrRgPt\nbsITLTBKwuqXiS5Z3vmnwPctuSMlvQSvqHg2njq/dU05TwdeZJ6hjaQT8O/BLngi27gU1Eup8rjF\nKoAWnFS8zcwWli1I1+/b8M+oMQNj3KlfI6UuCyTtbmYzofHiYynj829J61qKUZYnqDR5ZJoiaRkz\n+2eSkxMBVFK/pdYiHpa0RWe21DLqZhXzaCbkaeRNru02N+FSZaJLfk7TzeygiuwLJH3SzN5TjSSr\nwVq4m6gz014BWNPMnpDUL4O3iF4oVB7XCpXhThSZVFBusjSKgTHuVqYOe5Xq4qPwqoi5i4+ljM9H\ngMsldbLoXoj773PpRACdgn8x3gLUSroorN9eC6FNOtD3Wkia0UDO5/Cwv3NxveyD+8tzaXwTtkI1\n6jtonPoplpHEBPxV0gdxvzD4It39yXjkpMkfhy9gXoJ/3i8EPpnclj+f6MRSerGMzNw6SOppCywv\nianUpKLUZGkUA+dzrxiuUTT1nyY/rszsIUmrW0ZFRHnhprPw+ueQjI81qG4n717TyXy8ypqXH9iV\nkQigCywzAqi0fpPMdXC9fKbBuUtRibpJY2mSBTyN1PQYzxS8rYGMXfF6RKNuwmZW+4sm6Yhe+y0/\nWafq7lgWDxCYY3lJTKvhi47b43q5HK+l/gBeUnhuhqw18BLawksA39vnlO7zS+ml6rZaGl+IbJLE\nVC18tiwe83695SUxvRGvejlqUpF5g+gk3R1I5XsNfN08tLIxg2jc96psLouXub3XzA5tKG9lfAHo\ndXiDgLUyzy9ifCryNsKTMGZYy+xBSdsBrzOvTFf3nCL6TYZjb/y9rIX7dt+XI6MiS3ihrNfhae59\nW51NIGsF/D3ta2Yvb3B+q5uwpPdWNpfF3Qm3t13cTQuBbzezJk98rUn+5I0ZnV1au/zuJOrlVXgN\n9Q/3PXhiOSsDp1lmAbISk4oeMhtPlkZRN6xmcb3wcMSLMs9ZDn8E/SHujvkbnuiwRMMxCP8AvwH8\nqcH5a+AuiF/i5UGPBJ7bcCzPw4s4/RZvYHDIotIv7rd/I/7IOA93h8xv8b+3Bo4HfocvWu8PrNJA\nztJ4Q+iz8ZrYp+A3iTZ62QhfqM7uXNQlZxm8wFtjGRVZ12cePxX4DJ5ef1Hn1eD/vhVfOL0/XXOP\nNJEziXq5uoCMpUjNXhqevwJedfZ/G56/Gu7m/AXwG+Czbd/TwPjcJ2BjvMNJLeQldV+IP9p8Gb+g\n51pGZb+KrK3x2eSr8RjhdwLvzzj/bfjMdm3c8LwV+KFl+r8lbYL7ovfFkz++iz91lVgcytHvffgN\n6qN4HQ6T12PJQl6Wdx/cqJ8JHI3XgsnKIJZXcdwXL6VwMV5aYStr6OdNrofX4p/5psCnkvw2LE9+\n5mN3dNMSeOXB2r2EE6fj18orcD/u/g1kABwGPB83ojvJE9jaruE01Us1+mYJvLdrtvtB0o8q5y2B\n15g5O1PG0sDL8OtlVzwX5msZ5z8Jty2vw9uCfh/Y0MzWzhnHeAycca/41DrNZv+IL+DV5Tn4DON2\nPHPyCUlZH34p44NnOl6Fu05mJ9lN/GB3AJfhs9G5Sc67G8hpq98P4zeZE4AzJH23z/HjcSDeS/ME\n4Mdm9mhDvZyP62V7M7sLQNLxuUJK3YSTrGrI3hR89pzlV05Uo2YeB/4XNx45rGpmJ0s6zLwk7qWV\nRf0cHk2fESla6w5Jz8gRUFAv1eibx/En2D0ayPlsl5y7zWx+nRMLTiqKTJbGpcRj0aC98ISLo3ED\nchk+W3laxvkL8MWnhQ108WSNNo9ac/CsvnsayHk1PgO7B49K2Rm4azHqd0M8AugW3M30QWCTjPOn\n4Knx38ZLKJyGJ9csmTmOzXEX1W/w2kEH4F/S3PfzGL6IOr2yL/vzTuetV3mtlfueCn9OV6ef5+M1\nhDYn9enMlPN9PAHq4+la/iEw6z9VLwX0+u90vWxQ2dfEPrwbuAbvFfFh3BXY6Lrr9RrEBdVeGWQP\n4F/a3NoaSJqO32X3xv3DfROQUqjYS9J5L8Lvzi8G1mkyhiRzbUZcK8vjC5BZi0BpsfBVlXGdmuRc\nkCGjtH6fm8bzWjPbqMH5ncW1ffGojgvN7HUTn9VTznZJxl54A+/vm9lJNc+tLg6vjs/e32Rm2YWt\n1LtV20OWuQgvaWaP3Q/gZRFOtFTTv4+MV+CTm3XwMrkrAUdZyv1ogrxZxsrAT62SJFjjvFJ6+WKP\n3Q/gT9Y/zJDTK1mso9/3mnexGu/czfHv8mvwtaez8NaDTapcImlDUpAF7iY9Er9+72wib6HcATTu\nV+P+xZtx18FzgZuAVfEaKLUMmaTtzOyKyvYSwIfNrG7D4855RYxPl8xN8GiOxn7L9GV5Db6qnpNV\nV0S/SdZKjO7w89e656bzN7DkTqnI28u80l8j0ue8C66XbN9725uwyqX8H4+7Ls5Mu16Lu9CWA1Yy\nszfUHVNTxjHIC8n5vAvqpUgpBElH4SHOZ6TxzMBLYc8B/svMdqwpp/GkYhx5rSZLo2QNoHE/Cy8o\ndGvanoYvYh4DfM9qNoRQj8Ycvfb1kdHK+KhPzWvL7wvbCUlbh9FGtXY9jBL6lfR23O31CCOzHzOz\nrAWycT6j68xsy5rnT2qdkCY3YXkiyngp/8ebWa2Uf0m/MLMX9ton6VabIN59nNntQqxm2Kukf+Nu\ns84TXbVZeNbnXVAvFwEvsZFSCEtSKYVgZtNqyrmm+39KutrMtpF0k5ltVve9pXOXwJ/uZ1jznJxW\nk6VuBm5BFXhmx/AAmNltkjY3s3mSJjoPAEnb4rVfpnZFHKyE+3pzOA+f5XbG8qCkg/FQuzp0Fn+e\nmsZ0UdreCbiE+qnXAEg6Bm+sPY+RDMOsehi01G/ifcCzrXki1jPxjjwrd90AV6ISR12DTp2QZfGo\niZtwA7Qp7svcvsHYXsDonqN3jX90T0ql/E/V6GzZdfE1HPA1gok4CPfjno3PTmt/sF18CQ8hvgJ/\ngrjcms8GB6EUQpV/S9oHT0CC0Y3Za79HSWvh6whL4utP38oYQ0dGz8kSDaKJqgyicZ8jL0xUTZm+\nM10AdfxzS+MlcZdkdMTBg4z+AMellPHpuAUk/Rhvv/WHtL0GHkmTyz7ARjm+zh601S/4Amab/o7P\nwF1dT2Z09MNDZHS6shQKmp5GDjSzW9L2c/AbUBaSTsMXtW5kpOeoUbPEQ6JUyv978ZIVv8GN8wbA\nO9K6S7+orTXwNYTX4rPu7wLnWUqTr4uZHSa/4+8IvAH4kqQLgBOqT7Q1WeylELp4PZ5j8VX8M74a\n2E9er6lW4xlJn8bfx22Mvl5qJ3clWk2WxmMQ3TLL4UXvqynTX8XvistbpZBXHznrWapHkR6ZVjSz\nB2ueuwe+cLk7UF18egivEnhlzbfTkTeql2Uaz82WmaEq6TzcH3hfznldMlrrNy0onYLPjhfOluo+\n7lfkbGtmV+WcM46cUj1db8dvwo2/FCqb8r8M7l/uNETpu4jaQ8ZauA/3PcAHzey0XBlJzpNxv/Qx\n+NpVVi2hwnppVQqhFJLm4MXDcp4Yesn5KbCntWyIPUbuoBn3Ukg6A388fQLvtLIy8HnLSOktaHy+\njK+Cn4nf2WfgiVWHZF71r8wAAB6bSURBVMqZjoeh/YrRRjUrZbotkn6JfzlvoTLrsvwkpOOAT+CP\noz/F+1i+y8y+kynnTLx/5Xdw/e6H38yzEpAknQMc2nnCWtz0cBFhGXVL0prEvrg/+jq8HG3t9Pg0\nG94Dn51Oxd2I3zWze+rKmAy6XCFAXimEJGMq/pS4fpecnEbxP8FLRNeacE4gp8hkaYzcQTPuafX5\n44z98HIX6240s+dJej2wJR6LfZ3Vr/tczPgkWXsC/y9t/sLMvt9Axq3AiYw1qrUTU0roV9KV1rIz\ne5LT+YxejT8pvRu4uMFi1rJ4PkFnAfIXuOug1kxXI9mKT8LLO/yShjfPtAj7PsYajZx1kXFdRHW+\n8PJIkFfgiXxn4WGLTcJcH8YbUZ8JzKXLF50TEFBQLx1XyK1U1p1yJziSrsTDRK9jRL+YWd9EMXnR\nMcP9/5vhfR/aPMEWmSyNkTuAxv0O/EverfS/ZMq5Ff+ingF82cwuzV0FL2V8SiHpUjPboaWM1vqV\nZ/DeDfyI0Rd1bijkrWb2bElfx33CP20SqdAWeez2uGTePG/CU9C79ZvVi7WNiyhFucxjpDx1R0an\n4UetCY6kb8G4i4uWOcstpZdSrpBst13l3P0n+nuDJ9gik6VuBnFB9QEz+0kBOSfiqck3Ab+Q1+au\n5XOvsFT6+TLgTDP7a0ZEyUIkbYNHHjwLX/CdQoMypXgn+0/h6wBVo5oT8ldCv504/w9V9jVZ3f9R\nutk8gi8WTsV9/1lI2hivAzON0VULa40nx3jX4HEzO6H/YX35FR533cRF1KTx9BjM7E3AmJDgzr5M\ncaX0Mg//XrYy7sCPJb3MzGblnth2Rt2DiyUdSMvJUjeDOHM/Fjd+36O5ARtP9pI5j6dpLK/Cjc9W\neHTHj61mTG5Fzmzcz34OHrL3Rryp70cy5VzcY7flPNpOpn6bII/bfzCFsi2PJ+j8MVPG5fhi3Rfw\n6Js349f2kZlyGmctVmR8HE/M+T7tnmoupqWLqCKrbbJZq3yEdPzHKaOX8yjjCnkID6P8Jx4llt3G\nUL1b/3Wul0/UfRqW1CvyyHJd0WPkDqBxb2XAJO1nZt/ROD1DLbMXayHjM9vMpku6ufNIPFmPYjXG\nUuIG0aqLjaQXmdlFGifJK8eXm+RdZ2ZbSrrFzJ6b9l1mZv+v37ldclpnLZb6oo7nKsp0EbVKNtNI\nSPBxjK6GuhLwfstrHFJKL71cIpaz0FyKtCb3BH69gF8vwg389mY2UYvBSWfg3DLWo4ytpJzmDSuk\nn417UfYyPl3umNzM0n/Iy4PemC6IP1TGmTOu1t1sCugXvPxrh4VdbKgfD74DntDV6+I38vX7qDy8\n9NfyJLPf44ljueza9VR2kjxr8WhJtUoQmNkYd4XyG7OPMeJpIfx1jHSJqkPb+Oki+QhQVC+jXCJK\njS1y5XTJ2CjJ2NfywpO3M7PtKtu3SLrCzLaTtF/G/y/R8m8MA2fcO6irgxK+Mt0XMzsx/WxTb7q0\n8XkDXjP6YHwxcx38veXycOX3hd1sGshprF8A6wrhTLJqx0933CVWru/ou/A6MIficdg74bXLcymS\ntQggje4uhRcky0LS89L5++CZsrklf1slm5kX4vqhCoUEQzG9jOkC1kBGp1/vvjSv3b+ipK3N7Jok\ncys8gRJGSjbUoe1kqScD5ZaRJ9jsjn/wW+Cz71fhoYNZ/QRTeNwB+GNldZGtVVuvpqT3tq6ZzSko\ncxlgppm9NGMMRfTbJXcpPCnrWQ3OfTljP6Mmdb6RtIKZPdz/yHHP3xDPWtyWkazFd+NPAlua2eU1\nZPRq8DLTamaHqndjlvdZg4qDKpds1jokuIBeejW2eK1lNrbQ2Nr9Z+O1+7MXoeU9lr+JG3ThARtv\nxcM0X25mWc0/KnIbtfwbgw1AfeR0gzkdr1d+Mp50MYUWNcvxxctj8NnL/nhxoeMbyHk58AHgiM6r\ngYxX4n7bu9L28/ALu63OVgF+vaj1i6/qz0yvH+MRDMc2kPM1fHZyD74gegtwcgM52+Ip4L9L25sB\nX53M67XHGP4bjwm/EP+Cr9pEv4zUCn96ZV/T2vK/BD6PLzDv33k1kHNj+vlqvPTBU4CbFrFeHkl6\n+X+MTEqb1FAvVru/cv7KwJMLXkutWv51XoPklmndQamLp5vZ3pL2MLNT5Rmr5+cIkFeyWx5/jPwG\n/pj+ywZj+TgebXMJgJndKGn9XCFq182mpH4bd7Hp4gVmtmlaaD5K0ufId3kB/A/eFWcmgJndJOmF\nE58ygqQPmNlxGklOGYXVm+mW6i61Fz5zv1ieln4WNC789biZ9QwsyKRNSHApvZTqArYm7tL5fFpr\nOpuR91eL8YI2Ojqx/KCN1i3/ejEwxt3MNkur868Dfi7pPuBJkp5mmdEpiU4RrL/JC0n9Ec+Oy6GU\n8XnczB7I+EKMxyuqMvFm3bV8eyX1a+XiwjtJNv+QtCbuhmgUo21m93Tp94nxju1BZ91idpP/nXga\nIw1e/idFJS2nzPBb88zl72ukMcu7gdXlxd6yGrNQLn66TT5CKb18AfiCRhpb/ABYU16MrHZjC/PF\n5ROAEzRSu/8+edJY3dr9rYM2uig1WRpNqUeJ0i88HvxzeB/TKxuc/1bcbbED7ja4D3h7poxr0s+r\n8Tv+MtR0g3TJORk3qjfjNWa+BHytoV6mpLGs23ktav0Ce+KP2g/gfsaH8HDR3DF8DI/E2Au/+f4B\nOLqBnHPxksrX40li78MLvC2ua3dZ/CnvPOBPwBkt5T0FnwFflHneXT1eTV08qwBT0u/Lk9G2chL1\n8lzgkzRoHdhD1ibAkYvrmpmM10AtqIKHfFmBDkqFxvIx3BDvjJfoNeDrZtYzJHECOcvjPUdfgj9i\nn483zMjKxpR0CO6b/hOj62rk1MtprV9Jc/Fm3Y0idcaRuQzer/aBvgePPXc1fCH0xbh+LwAOs/yS\nFa3rn6hgdym1bMzSll4hwVUsr7ZM0a5bapiYNd57qcjJeU+ti49VxvRpPHxXNEio6il3AI176w5K\n6Zzf4DPuy/BokNrV8MaR19j4lCIZ1a1zjVaXjBIdqq6w0fG9TcdyGV7k6zLgCjN7qK3MluNpXf9k\nHP1mZXOmc3o2Zsm80bRNNjvKzI6U1MsAW44RK6iXtolZnffSs4GOmU1o/LtkNS4+1iWn+GQJBsjn\nrrIdlMAXJbbGV9c/m/zNN5nZqzPG1G18Ghn2EjPCxD2MdKDJHUNJ/c5OC1o/YLQvN3c9Yn+8vvde\nwGfknXQuM7N35wgpNYOiRf0Tlesu1aFEY5ZW8dNWIB9hEvTSKjHLyjbQWd7MPthkHF38qbRhhwEy\n7hTooNTFE/ii6hP4zOdPuN89hyLGBw/L/BoecZOz0NfNPOASSf/LaKNaZ3W+pH5XwpNjXlLZl53c\nZd7a7xE8PO0xfPaUHSuP17i/DO/Ek61fjTSC/pGkd9Cs/kmxbM7Er5Ksxo1ZrGWyWde5TfMRSuul\nbRewDuvb6Lr9f8L97jk0Lj7WRanJ0igG0S2znjXsoNQl5x943PTngZ83dWWkO/oO+BPATngs9a6Z\nMrIfP8eR07MQluU1cG6lX3lbtEPNoxdakVxnf8Zrc1yGx1NnJ1OpRfnWdP5d+M2pVzhT7Uf+JKtU\ng5fijVnUMNlM44QEm9kBGTJK6aVUYlbjBjoaKTAnWhYfS/Jau716yh1A4966g1KSswc+694KnxVe\nifveL8yQ0cr4VGaEh9KyIl4yqsea2fv7HjyxnBIdqi62HjVqGozlMPwzWge4A08u+YWZ/SZTzifw\niJ9WMyhJy3Yvcvfa10dGqe5SJRqz9IyfNrPDM8dys42EBG8qaUXge2b2kr4nj8gopZdijS1UoIFO\nW0pOlsZgizg8p9+LkWy41+Oz7s5so6m8Z+KxwncDj2SeexjuUrka72r+ZtwPWvf8u3BXSpGQNODC\nQdAvnnX4ZfyLsUXn1WJMKwKHpM/oiYzzHmIkFPPfuOFoE5p5fZ19NfWbnc3ZJefSAp/1DpXXdsDa\nDeW0DgkuqJfssOjJevX6Pjb5juINgIqPb5B87h2WSo+Pr8I7KP1LDTLa5HWfn4e3B7sMr6F+TY4M\nMzseOD7NVN6MZ5quTc0FSKtZr0LSLmb2sxqH3ihpJn7DWVhDxfJ8cyX02ylVXPW5GpDbMu1z+Mx9\nReAqvLzDZXXPN7NaSSSSnm1mt07w96fhBaiWS4/9HffMSrg7IociDV4o0JjFyiWb/VjeIPsz+IKs\nAVkNsimnlyKJWWrRQEdet2oFYLUUrlq9XtbMGUfiyuQm+i6jv9etwl4H0biX6KAEcCw+6+q5wFbH\noLY1Phl8Gqhj3J+CZ3FWjWjuQmZr/VoBl0ziauA4M/tTrz/2M8oZnIY/XYzHS/Gww7XxxK7Ol/VB\nPO09hyLdpYDN089tKvuybqCl4qfN7Jj063kpyqRJSHApvZTqAvZlejTQqXnu2/FKpGvirs3q9ZIb\ncQOFJktjWNyPNjUfW5acBJl9H7fxGhSrT/D3Zxcayw3/SfrFy7SeDPwkbU8DDlgcn1Ep/eI+6dcX\n+n+tszkLjWMu8KwCci7DXXG7Ak/6T9dL+v+z08+bK/tqu3zwmf7HFtf467wGZuauPh2UcP9w0X/Z\n7wAzO6fPIf1mhHWp5RZJ8fIn4Dec50jaFNjdamSXFtbvt/CIhU6bwDvxR8qTM2TUoXUxnkRf/ZrZ\nv1OCzOlN/oEKN3hRgcYslIufbhwSPAl6KdXYolUDHfPCey/DK8+2Ql7A7JPAmma2m6RpwLZm1ur7\nNDDGnfLFePpRIkyolPGpy9fxdmedhiQ3p+iXOqUDSup3NTM7W9KH0jgel9Qmfn88FnUo188kvY+x\nvs86/twdKNvgpURjliLx09YuH6G0Xko1tijRQOcCSXvhkUNtrtVvMQmTpYELhVxUqEFJg8mQkeR8\nz2qkPUu61syeL+kGM9s87WsV490ESZfgX4SfmdkWaXHq02a2Q+H/01e/8mng2mZ2zwTHXG1m24z3\n98pxd/XYbdayUXEJlNmYJZ1TJH66VD7CZKAWjS3UsoGORppsP46vHzSNc5+U7/UgzdyBhSvRi6KD\n0m8Ly5sQSS9gbHr8t9PPuvUs/izv92hJ5mvwx8mccZTQ73vwKI6NJF2B15XfO3McfY0yPkucEDMz\nST8Axk0Sq2PY03ETRjfVjWpSwe5SFZYnY9EwxU/fbGXip7+Iu2X2xRd6L5XUJB9hMvTyDzwZKQtJ\nr8RL7S4NbCBvaXh0zk3C+kRrZQQEPCxpVUa+1/+/vfMNuayqwvizZuyPokFpZmUOIqGMNDPCWCJ9\nqPwfNmVCpUU2loWJlRBhUPpOUSjkB+kPodYYqWQYhH0wQ5lUskkdnZlm1FHTUQzMsBwnA1FbfVj7\nzJx7u+979z77Oe9d97Z+cNFx7t13u973rLPP3ms9z3HoKDPSxl1yh+1jPwyrYPgWrB67q09o54TK\nSj5prJ8DOALAZuxtj1eUP0peAOAqAEeJyF9h9fKfLByDEd/tsMftI2GrlR2wR9xsmEkZwEYROVZV\n7y2ZQwfGVjUJyeBF6oxZmj3hNQCqk7tWlgQD1LiwjC3mQDDQGUPumVz1YmkU7rZlmkeTVjfcawDc\nqoUiW/MlVC1oUxaebMBDMJGiqmBLkk0VM3JYoqq7ZUhKNWOM6viO2i7pskUlIj8EcG1tUhaRB2G6\nIE/C9qqbx+NsKeTM79nz2LzAe6q7OdM4y1p/LDJmaY3xHVgHclX99IiS4LtgB6qPF4zBikt766+z\nsYWI/ElV3zO0FbKV+TuT8/uS3vc6WI4aWCyp6ksLfnAMHlfuDAclwGpXaxMqa0W4DeZIU7SFMoJf\nwTpB24dtN2GB1e8IOsdXuM0+gK3iviAitUn5tA7f3YWc3yWKu5SqPpm2Vt4Cu07fJiJQ1acKhmHV\nTzP6EVhxYTVmbRORswEsFZF3wiRC7iaN3ZCbe/6YFkZ7Yigi96OyEs9jcr9KrOvrm7BHlf3Tv5fC\nSKis5HMQgAfFdDGKRaCEK5taE9/5mn12o7zZByAlZd0rhHYwusnIMmF0c0LmMWYBkP27p6RmM1JJ\nMCsuLGOLC2HVKS/BxMNuBaGssYQeFkuD43vblmEh5tW4Crav10lVb+jReA9NMikYZ2QVSe4qREwE\n7SMA1iCZQCd2w+zk2CuOcfM5UwsNCcaMN5CUC1enSHvLV8A6Bp8FsAzmHn80a47pe7Kqmlrvr3GX\nYhiz9FI/PeJ7srYfWu+vjQvd2KLDPKqrtETkHNhiaTWAezG4WLpWZ1Dyl+KgVJtQh8aqSj4shCCb\nyoivmJrjetgv4dWwVdvFWmbeTEvKYg5KH4BJOx8jIu8HcJaqfr5knDTWvIfwmZ+nuEulxclJpfvs\nQ2PcglQ/rWaQvg+sW/ddXcec53tySlZZcWG5gDEsFVlnctTFUoPHbZlqByWAszc3X/KBbZGUjNNZ\npGiIM8SkYGtkUxnxPVdVrxSRU2CPx2thSaQoucMeg4/DUFIuHAMAXlbV50RkiYgsUdUNInJ56SCk\nqqbO3ZxD1BizNCxWs1kOrLiwjC0YBjqsM7lDxTxhqxZLw3hM7gwHJVZCZSWfUSJFxbW5AE5W1a+J\nyBkAnoaVS20AUJLcGfFtHh8/CGC9qm5Jj6mlUJIy7HB4f9iq8HoReRZWSVFK9SG88tylnkqv16ZX\nF6rrp3O2H5DXj8CKC8UFDBWWii1YZ3KsxdIAHpP7C9jroHR1xZ4jI6Gykg9U9TERWaqmUrlezFy3\nFIZsKiO+m0Tkd7Bqh6+LyAFoGScUwErKd8Js3L4M4FOw8r8uzTHVh/Ay2M35EwAXamE3Z6qS2V8r\njVlAqJ9W5fQjEONS1ZglHEvFBlaVFmuxNIDH5H4W7PHtiwA+l5JgkYNSAyGhspJPlUhRC4ZsKiO+\nn4UdVj+uqv9Oq8M9JsqZpXEALykLrNrhHwB+AeDGjjetqqqmRHU3p1oDEkOQrrrZLMHYfmDFpbYx\naxMGLRXbN9Ai6WBilRZrsTSAuwPVhrQXfBpMN/lgVd238PN3AjgRtqf2DCyhfkZVVxaMcQmAn6bP\nNsnn+tLEkapu/gZ7vL4ojfMjVX2sZJw01hthLkOvijUzHaCqz6S/yzX9qI7vmLGzGprEPGE/hr1J\n+ab5aqkzv3cFgI/D9nWfVtUTCz/PPIRvujm/CtvWyO7mTJ+/Avak2dmYZdTPIfdnM/QZWpMYIS6U\nxqyM78nxe2AVBCzB3sXS82mx9HZV3Zr+vpOvgbvkLv/roHQXzOaraIXKSKjM5COVIkWZ35FTtUCJ\n75jvKC2Nq0rKrXEOgW07fAJ206N2qGbOobqbM43TWfRL9tZPXwczt2jXT/9YVY8qnEt1STAxLhtG\nT6Wsgz3je3KuJVqVVu1cRqIOROXbL5ik59IF/v6kgrH2BXAkYU4rYGYFD8N+kKWf/xDskfiJ9OdV\nMIU/duxyTClo8V1gjFLf0UNgTSV/QAe/XADnwzRCtgNYBzsU7TLv42D1xv+CHfq9ikIvViySwcuY\nOZwDO2jfDZPb3ZBeNwP4aMW4BwM4rHlNW1wK55tzLTWGH1tgcgEAcM8k5jLyc5MOYof/0azEwUyo\nhOSzCfbk8EDrv3U2/a6NjZcxiEn5MgCrCPO+D2a19gCsumotgO9O4mcE2wa5HcC29OcVAL5R+F1n\nkua8BsCjsG2QJ2D7wdsnFBc3LmAAboM9ifwA1uV6JXow8O56TXY5XJk0uafIczDVt+cBU31DoUaN\niJwvpl1+O+yw7Tzt9qj/inboxpsQC8ZXjHeMGSNLLRO2R/kVVT1aVS/Vjg1rqnpx+vlWo7Ztt1RV\nX1XV9QDexxi3Re7v79Uwn9CX07y2wrabSjhURN6QfmbXiMj9IlIk1JVoSoIfUZNFPgG20GGSG5dr\nYYfnjRH1I7Bzo0nQLgj4LYC/YLQpyUSYxuSee0jASKiU5IMhkSIR+T74IkUAR6N+wfiqLSV+PeY9\nufrptKRMYqCqSUQuQreqpoXI/f3dT1WHJXFLK7XOVdUXYDXhTf30ZYVjAKkkGMCekmDYkzCT3Lgc\npKq/RKomUevg7aMxa2fGe5oqrd/DVvDFVVrkxdIAHkshWVSrvqnqxaS5tEWKbkCFSJFwTD9qWSz9\n9MWGYb3GotqYBbz6aVZJMAOasUXttaSq6wCsaxUE3CEiRQUBqlRfgwGmMbnvzHwfLaESWJ5e+6TX\nh2H7mEVbPMIz/ViInRnvYXXmuUJNZndfAG9NF24RKXFSDF7AMWZh1U9X9SOQ40IxtiBfS8/Cyq2f\ngz0hldLLYsldKSRQL96UxlgNS+7tcSaSgERkB6yudxtaF5eWq0uyTD9qxbEoapnekJb1mqoeLh2s\n14QnJsUwZqHUTzNKgolxoRhbMK4lETkftmJ/M8xX4cYuW7fMPoI27lbuxDvq9RiRUCfE31X1N4Rx\nGO3x1fFVX/rpTOZQb73GWoVVG7Ootfff3/rzc7DVZUOWDRxj+wG8uLCMLRh+D82ZXO25US9mM+6S\nOzgOSgAvoTK4VESugVXd1CjZMdrjq+M7X2ceCtUyHfKKqu7qti29h6otK+Eas4z9usL312w/1MaF\nbWxRfS2xzuT6Wix5TO4sSzpWQmWwFsBRMOGvtqtO6VzmCHNhxJellukNhvVa7SrsSACnw/a422V1\nuwGcVzn2MFk3+BHbD+d12H6ojQvbBWyucj40+losudtzF4KDUhrnOlhC3Y5WQtWM9m02IvJnJRsk\ndIURXxG5T1VXp/brY1T1PyJyj6q+mz/jxUNE9oOd0zS14LcC+Hbpfm4aq9ZdqtqYJeM7cjWALoM5\nflWXrRLi0ouxxSTpS8bA48p9jjTOSi8JFbbfuLyiTh4ATaN+rmYOCU+lcUyqq5qIq7AqYxZmhQpj\n+4EYF4qxBelaYkGTFh9Aya2yXl6wH3yndvYe5vIQ7ELaAWArTE+9i4xB7+3xmfO4BPZ4vBSmY/Il\nAAdOOs6E/68dsK2Qw2HJZxmAZYVjbAFwIJLUBGyv+aoOc9mc/nkGgJ8BeBPMMatkjE2TjmkPcdmS\n/nkKrCRyJTq053u5ltJcepExcLdyJ95R3wvgHBF5Arb9MMla7FNZA2mlRj0pviz9dG8wDuFZqzCG\nMYunZjNWXGjGFrXXEhGWr8EA7pI7eJZ0tIRai/LqvxmmH9XxVU5pnEcYh/CsLSuGMYunZjNWXFiN\nWSwDHQa9LJY8Hqg2h3Vbm19CEblbVY+f9NwmjXA06mnxFQf66UwYh/BCMnhJY1UZs3hqNmPFhdiY\nRTPQYSEkX4MGjyt3T3dUV2hle3yiOr6k0jiPMA7haaswVf1n699fRMt5CMDlABZM7uqr2YwSF+U1\nZjGuJTa1MgYDeFSFbIs3vYjJije5Qqw9fjOsegIiskpEbi4chhFfllqmNzaKyPKaAVR1nZrN2gWw\nypA7ROQ2yuwGGbvPLCJrRORRmC7NHTDdoFt6mMtYPMUFoF1LFIQnLT6Au5W70zuqF+ZQ2R7PiK/y\n1DK9wTyEp67CRpCzn+qx2cxDXACO1AQLlozBAO5W7p7uqA6p1qiP+C7IqbDD5ZNhJZGno9B8oa9V\nWEcWQ4c9C2dxARwZ6GhPvgbuVu7wdUf1BqM9fg4R35GQDhp7WYWNYGfGezw1m1XHhdmYBc615Bp3\nK3c4uqM65EJYR1+jUb8LVhtbQsS3R5irMBE5XkTOFpFPN6/W9+QYs7ixgWPERa20j+ICBs615BqP\nyX2xLOmmkXZ7/Oth7fGlDSoR3ylATJr5e7BzgGPTa3XpMKi0gXPIRhE5ljAO41pyjcc6d5p406wh\nBNOPiO90ICRjljQWtX56kgjJ2IJxLXnH4547xZJuRmG0x0d8pwOW9DXQf4XKYsIytvDk99ALHlfu\nM39H7YqInAArZevcHh/xnQ6EI81MsYHzyHBjlpZLB1dfS97xuHKf+TtqBQzTj4jvdDBHGGOxKncW\nDeFJB7MMdNziceU+83fUrgjB9CPiG0wzQjK2YFxL3vG4cp/5O2oFDNOPiO8UIL7MJDzBkg6mGOh4\nxmNy9+Sg5A1Ge3zEdzpgSV/PGqzGLE9+D73gMbnP/B21AoZGfcR3SlA/ZhKeYBlbuPF76AuPyX3m\n76hdIVW0RHyng5C+Hg1LOnjmq8M8Hqi6MRiYRSK+04FHMwlPzFJjVl+4S+5BEBhJmvkwVd0x6bl4\nY9ZcwPrAo7ZMEPzfE9LMo3EoHewWj3vuQRCENPN8zFxjVl9Ecg8Cn7yiqrtMwjxomGEXMDqR3IPA\nJzNvJhH0S+y5B4FPZt5MIuiXSO5B4JOZN5MI+iVKIYPAISHNHNQSe+5B4JOQZg6qiJV7EDgkpJmD\nWmLlHgQ+CWnmoIpI7kHgk5BmDqqIapkg8MlGEVk+6UkE00vsuQeBQ0TkIQBHAAhp5qATkdyDwCEh\nzRzUEsk9CIJgBok99yAIghkkknsQBMEMEsk9CIJgBonkHgRBMINEcg+CIJhB/gv4EPb1igsDoQAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7853e6fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "traini = pd.concat([X_train, Y_train], axis=1, join='inner')\n",
    "\n",
    "# Find most important features relative to target i.e finding correlation of every individual feature i.e independent variable with dependent variable and then sorting them and using the features that have maximum correlation\n",
    "print(\"Find most important features relative to target through correlation\")\n",
    "corr = traini.corr()\n",
    "corr.sort_values([\"class_label\"], ascending = False, inplace = True)\n",
    "\n",
    "#Selecting top-20 features\n",
    "corr.class_label[1:20].plot(kind='bar',colors='RGYBC')\n",
    "top_features=corr.class_label[1:20].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top 20 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mean_waistAngularVelocityZ', 'mean_waistAccelerationY', 'var_waistAccelerationX', 'mean_headAccelerationY', 'mean_sternumAccelerationY', 'mean_waistAngularVelocityX', 'var_sternumAccelerationY', 'var_waistAccelerationY', 'var_headAccelerationY', 'mean_rthighAccelerationZ', 'var_waistAngularVelocityZ', 'mean_sternumAngularVelocityZ', 'var_sternumMagneticFieldX', 'var_waistMagneticFieldX', 'var_waistAngularVelocityX', 'var_sternumAngularVelocityZ', 'mean_headAngularVelocityZ', 'var_rthighAccelerationZ', 'var_sternumAccelerationX']\n"
     ]
    }
   ],
   "source": [
    "features=[]\n",
    "columns=top_features.index\n",
    "for col in columns:\n",
    "    features.append(col)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select same features in Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "Index(['mean_waistAngularVelocityZ', 'mean_waistAccelerationY',\n",
      "       'var_waistAccelerationX', 'mean_headAccelerationY',\n",
      "       'mean_sternumAccelerationY', 'mean_waistAngularVelocityX',\n",
      "       'var_sternumAccelerationY', 'var_waistAccelerationY',\n",
      "       'var_headAccelerationY', 'mean_rthighAccelerationZ',\n",
      "       'var_waistAngularVelocityZ', 'mean_sternumAngularVelocityZ',\n",
      "       'var_sternumMagneticFieldX', 'var_waistMagneticFieldX',\n",
      "       'var_waistAngularVelocityX', 'var_sternumAngularVelocityZ',\n",
      "       'mean_headAngularVelocityZ', 'var_rthighAccelerationZ',\n",
      "       'var_sternumAccelerationX'],\n",
      "      dtype='object')\n",
      "19\n",
      "Index(['mean_waistAngularVelocityZ', 'mean_waistAccelerationY',\n",
      "       'var_waistAccelerationX', 'mean_headAccelerationY',\n",
      "       'mean_sternumAccelerationY', 'mean_waistAngularVelocityX',\n",
      "       'var_sternumAccelerationY', 'var_waistAccelerationY',\n",
      "       'var_headAccelerationY', 'mean_rthighAccelerationZ',\n",
      "       'var_waistAngularVelocityZ', 'mean_sternumAngularVelocityZ',\n",
      "       'var_sternumMagneticFieldX', 'var_waistMagneticFieldX',\n",
      "       'var_waistAngularVelocityX', 'var_sternumAngularVelocityZ',\n",
      "       'mean_headAngularVelocityZ', 'var_rthighAccelerationZ',\n",
      "       'var_sternumAccelerationX'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Selecting Features for training dataset\n",
    "X_train=X_train[features]\n",
    "print(X_train.shape[1])\n",
    "print(X_train.columns)\n",
    "\n",
    "#Selecting Same Features for testing dataset\n",
    "X_test=X_test[X_train.columns]\n",
    "print(X_test.shape[1])\n",
    "print(X_test.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Models and Comparing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating training and cross-validation dataset\n",
    "X_train,X_CV,Y_train,Y_CV=train_test_split(X_train,Y_train,test_size=0.30)\n",
    "\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "results_sensitivity={}\n",
    "results_specitivity={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 91.1764705882 Specificity 97.2727272727\n",
      "Error Rate:\n",
      "False Positive Rate- 2.72727272727 True Positive Rate- 8.82352941176\n",
      "Confusion matrix, without normalization\n",
      "[[107   3]\n",
      " [  3  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.97272727  0.02727273]\n",
      " [ 0.08823529  0.91176471]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f782d8fcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+hXbJHy/k0+3JQmoV4J6ImJ1v474i9mljSb8jO0RsD4wuWHZnfsb8m5Leyfdh\nF2DTgvGmDvm2JxWxLSuSQ8mK9VVEDCyckQfPl4WzgEci4sA67QYCy+ssXQHnRMTVdbbxi6XYxg3A\nPhHxoqSRwNCCZXXXFfm2j4mIwvBCUs8Gbtfq4cM3W57GAttI6gUgaWVJvYHXgfUkbZC3O3Axr/8X\n2c+L14zfrAp8TtYLqjEaOLRgrKpr/iMK/wfsK6mtpFXIDhWXZBVguqTWwPA6y/aT1CqveX3gjXzb\nR+ftkdRbUrsitmMN4J6SLTcRMSPvcdwmacV89mkRMUnSkcADkmYCY4CNF7GK44BrJB1GdpfNoyPi\naUlP5l+5P5SPK/UDns57al8AP4qI8ZLuACYAU8kOMZfkdOCZvP3L1A6/N4DHye5fdVREfC3pL2Rj\nTePzm+vNAPYp7r+OFcvXvplZUnz4ZmZJcSiZWVIcSmaWFIeSmSXFoWRmSXEomVlSHEpmlhSHkpkl\n5f8B2TW8zDcdRJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7829fdb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Applying Logistic Regression\n",
    "log_reg=LogisticRegression()\n",
    "log_reg=log_reg.fit(X_train, Y_train)\n",
    "log_pred=log_reg.predict(X_CV)\n",
    "score=accuracy_score(Y_CV,log_pred)\n",
    "#print(score)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, log_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Logistic']=sensitivity\n",
    "results_specitivity['Logistic']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,log_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 97.0588235294 Specificity 97.2727272727\n",
      "Error Rate:\n",
      "False Positive Rate- 2.72727272727 True Positive Rate- 2.94117647059\n",
      "Confusion matrix, without normalization\n",
      "[[107   3]\n",
      " [  1  33]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.97272727  0.02727273]\n",
      " [ 0.02941176  0.97058824]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7829fe8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7829fe8198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Decision Tree\n",
    "decision_reg = DecisionTreeClassifier(criterion='gini',\n",
    "                                            max_depth=10, max_leaf_nodes=23, splitter='best')\n",
    "decision_reg=decision_reg.fit(X_train, Y_train)\n",
    "decision_pred=decision_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, decision_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['DT']=sensitivity\n",
    "results_specitivity['DT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,decision_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 91.1764705882 Specificity 95.4545454545\n",
      "Error Rate:\n",
      "False Positive Rate- 4.54545454545 True Positive Rate- 8.82352941176\n",
      "Confusion matrix, without normalization\n",
      "[[105   5]\n",
      " [  3  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.95454545  0.04545455]\n",
      " [ 0.08823529  0.91176471]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7829fe2588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TZzP7I9EuYn1gQsK8Z+Mz5j83s/nxNhwOdEkYb2oUr3tOEuuSJCmUJFk/unvX\nxAlx8KxKnAS87u4nlWvXFaiqs3QNuNndHyy3jgu3Yh2PAwPdfZaZDQX6JcwrvyyP132euyeGF2bW\ntpLrlQpo902q0hTgF2bWAcDMdjCzjsBsYFczax+3O2kzv/8m0deLl47fNAS+J+oFlZoAnJ4wVpUX\nf4nC28BxZlbXzBoQ7SpuSQNgqZnVBgaXmzfIzHLimtsBn8XrHh63x8w6mlm9JNYjlaCeklQZdy+K\nexxPm9l28eRr3H2OmZ0JjDezZcAkoPMmFnEB8JCZDSO6y+Zwd3/XzCbHh9xfjceV9gDejXtqPwCn\nuPsMM3sGmAksItrF3JJrgffi9h9RNvw+A94iun/V2e7+k5k9QjTWNCO+uV4RMDC5fx1Jlq59E5Gg\naPdNRIKiUBKRoCiURCQoCiURCYpCSUSColASkaAolEQkKAolEQnK/wdYi8KLQp4NDAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f782a06def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gaussian Naive Bayes\n",
    "naivebay_reg = GaussianNB()\n",
    "naivebay_reg=naivebay_reg.fit(X_train, Y_train)\n",
    "naivebay_pred=naivebay_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, naivebay_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['NB']=sensitivity\n",
    "results_specitivity['NB']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,naivebay_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 0.0 Specificity 100.0\n",
      "Error Rate:\n",
      "False Positive Rate- 0.0 True Positive Rate- 100.0\n",
      "Confusion matrix, without normalization\n",
      "[[110   0]\n",
      " [ 34   0]]\n",
      "Normalized confusion matrix\n",
      "[[ 1.  0.]\n",
      " [ 1.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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KdXV1nHrySdz3wMN07daN3YcNZcSIg9ihb9+iS2t2bim1IpLosN76ANQtXcLSpUuRRF1d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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822dc3da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hwCOSFgKTgH02sohLgbslnUd2l82LIuJpSU/mh9z/no8r7Q08nffUPgTOjogX\nJI0EXgLmkO1ibs7/AZ7J209l3fB7HfgH2f2rLoyIjyX9hmys6YX85noLgFPr9tOxuvK1b2aWFO++\nmVlSHEpmlhSHkpklxaFkZklxKJlZUhxKZpYUh5KZJcWhZGZJ+f83EoQDBY9NJAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822dbd4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Support Vector Machines\n",
    "svm_reg = SVC()\n",
    "svm_reg=svm_reg.fit(X_train, Y_train)\n",
    "svm_pred=svm_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, svm_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "cm=confusion_matrix(Y_CV,svm_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 88.2352941176 Specificity 96.3636363636\n",
      "Error Rate\n",
      "False Positive Rate- 3.63636363636 True Positive Rate- 11.7647058824\n",
      "Confusion matrix, without normalization\n",
      "[[106   4]\n",
      " [  4  30]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96363636  0.03636364]\n",
      " [ 0.11764706  0.88235294]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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+3zqMkWecz0cfTOdXZ41k0YJP6b1Tf/75iutp265d0eU2GfeUWrDR11xNn759iy7D1qJd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\nOJTMLCn/A4TbjKhIIeUXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f782a09f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0S7Kqmtn0+Pl7wGNEN5yb7+4T4/kHAXsDH8Q/tlIJmAC0B+a6+5cAZvYUcME2\ntnEUcDaAu+cDq+Nr/BL1ih/T4unqRCFVAxjl7j/G23gliX3qaGZ/IjpErA6MS1j2XHzG/Jdm9nW8\nD72A/RLGm2rG256dxLYkSQolSdY6d++cOCMOnrWJs4A33f03Rdp1BkrrLF0Dbnf3R4ps4/Kd2Mbj\nQB93n2FmA4CeCcuKrsvjbQ9y98Twwsyal3C7UgwdvklpmggcYmatAcysmpm1BWYCLcysVdzuN9t5\n/dtEPy9eMH6zO7CGqBdUYBxwXsJYVeP4RxT+C/zKzKqaWQ2iQ8UdqQEsMrOKQL8iy/qaWU5cc0tg\nVrzti+L2mFlbM9stie1ICainJKXG3ZfGPY6nzaxyPPtGd59tZhcAY81sGfA+0HEbq7gMGG5mA4nu\nsnmRu08wsw/ir9xfi8eVOgAT4p7aD8BZ7j7VzJ4FpgPziQ4xd+QmYFLc/hMKh98s4F2i+1dd6O4/\nmdmjRGNNU+Ob6y0F+iT3X0eSpWvfRCQoOnwTkaAolEQkKAolEQmKQklEgqJQEpGgKJREJCgKJREJ\nikJJRILy/1WyyPbx5jY4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822dc7320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying AdaBoost Classifier\n",
    "ada_reg = AdaBoostClassifier(random_state=2, n_estimators=500)\n",
    "ada_reg=ada_reg.fit(X_train, Y_train)\n",
    "ada_pred=ada_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, ada_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Adaboost']=sensitivity\n",
    "results_specitivity['Adaboost']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,ada_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 94.1176470588 Specificity 95.4545454545\n",
      "Error Rate:\n",
      "False Positive Rate- 4.54545454545 True Positive Rate- 5.88235294118\n",
      "Confusion matrix, without normalization\n",
      "[[105   5]\n",
      " [  2  32]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.95454545  0.04545455]\n",
      " [ 0.05882353  0.94117647]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822c4e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822c1ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gradient Boosting Classifier\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01}\n",
    "gb_reg = ensemble.GradientBoostingClassifier(**params)\n",
    "gb_reg=gb_reg.fit(X_train, Y_train)\n",
    "gb_pred=gb_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, gb_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['GBT']=sensitivity\n",
    "results_specitivity['GBT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,gb_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 91.1764705882 Specificity 96.3636363636\n",
      "Error Rate:\n",
      "False Positive Rate- 3.63636363636 True Positive Rate- 8.82352941176\n",
      "Confusion matrix, without normalization\n",
      "[[106   4]\n",
      " [  3  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96363636  0.03636364]\n",
      " [ 0.08823529  0.91176471]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822bcf160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AiKgA5qXX+OXaM328kk63IAmptYAHIuLrdBsP5bFPPSVdQHKI2AIYl7PsnvSM\n+XckvZfuw57AljnjTS3Tbb+dx7YsTw4ly9c3EdE7d0YaPPNzZwGPRcQvq7XrDayqs3QFXBwRN1Tb\nxnErsI3bgEERMUnSMKB/zrLq64p02yMjIje8kNS5ltu1GvjwzVal54AdJXUFkLSGpM2AycDGkrqk\n7X65jNc/QfLz4pXjN2sDX5L0giqNA4bnjFV1SH9E4f+An0pqLmktkkPF5VkLmCWpKTCk2rLBkkrS\nmjcBpqTbPiptj6TNJK2Zx3asFtxTslUmImanPY47Ja2Wzj4zIt6WdAQwVtIc4Gmg51JWcSxwo6QR\nJHfZPCoinpX0TPqV+yPpuNLmwLNpT+0r4OCImCDpbmAiMIPkEHN5zgKeT9u/RtXwmwI8SXL/qiMj\n4ltJN5OMNU1Ib643GxiU338dy5evfTOzTPHhm5llikPJzDLFoWRmmeJQMrNMcSiZWaY4lMwsUxxK\nZpYpDiUzy5T/B1nFs11XFI3nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822c23e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Random Forest Classifier\n",
    "rf_reg = RandomForestClassifier(random_state=1)\n",
    "rf_reg=rf_reg.fit(X_train, Y_train)\n",
    "rf_pred=rf_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, rf_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['RForest']=sensitivity\n",
    "results_specitivity['RForest']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,rf_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Majority Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 91.1764705882 Specificity 96.3636363636\n",
      "Error Rate:\n",
      "False Positive Rate- 3.63636363636 True Positive Rate- 8.82352941176\n",
      "Confusion matrix, without normalization\n",
      "[[106   4]\n",
      " [  3  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96363636  0.03636364]\n",
      " [ 0.08823529  0.91176471]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822b986d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AiKgA5qXX+OXaM328kk63IAmptYAHIuLrdBsP5bFPPSVdQHKI2AIYl7PsnvSM\n+XckvZfuw57AljnjTS3Tbb+dx7YsTw4ly9c3EdE7d0YaPPNzZwGPRcQvq7XrDayqs3QFXBwRN1Tb\nxnErsI3bgEERMUnSMKB/zrLq64p02yMjIje8kNS5ltu1GvjwzVal54AdJXUFkLSGpM2AycDGkrqk\n7X65jNc/QfLz4pXjN2sDX5L0giqNA4bnjFV1SH9E4f+An0pqLmktkkPF5VkLmCWpKTCk2rLBkkrS\nmjcBpqTbPiptj6TNJK2Zx3asFtxTslUmImanPY47Ja2Wzj4zIt6WdAQwVtIc4Gmg51JWcSxwo6QR\nJHfZPCoinpX0TPqV+yPpuNLmwLNpT+0r4OCImCDpbmAiMIPkEHN5zgKeT9u/RtXwmwI8SXL/qiMj\n4ltJN5OMNU1Ib643GxiU338dy5evfTOzTPHhm5llikPJzDLFoWRmmeJQMrNMcSiZWaY4lMwsUxxK\nZpYpDiUzy5T/B1nFs11XFI3nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7822b16f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying Majority Voting Concept\n",
    "majority_class = VotingClassifier(estimators=[('lr', ada_reg),\n",
    "                                    ('gnb', gb_reg),('naive_bayes',naivebay_reg)],\n",
    "                                    voting='hard')\n",
    "majority_class = majority_class.fit(X_train, Y_train)\n",
    "majority_pred=majority_class.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, majority_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['MVoting']=sensitivity\n",
    "results_specitivity['MVoting']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,majority_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rBpZdw2MHERunl8+UVUl+GjgHeH9fy3MysCeZ5M4k70nytT53nte3r0zyZ337HyX51hwD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\nGmS4S1KDDHdJapDhLkkNMtwlqUH/D06UIgp+j34rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7821dc9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_sensitivity)), list(results_sensitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_sensitivity)), list(results_sensitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m+aisSvLTwFnAJX0tz8nAnmSSO5O8J8lX+9x5Xt8+leQv+vY/SfLNOQZQs6uqI+IHuH9E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\nQYa7JDXIcJekBv1/u48ms7tWCdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7821d842e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_specitivity)), list(results_specitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_specitivity)), list(results_specitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
